2 citations · 2 across the 1 of their papers we have counts for
3 papers
stat.ML2024★ 2 cited
Scalable Bayesian Inference in the Era of Deep Learning: From Gaussian Processes to Deep Neural Networks
Javier Antoran
Large neural networks trained on large datasets have become the dominant paradigm in machine learning. These systems rely on maximum likelihood point estimates of their parameters,…
stat.ML2022
Deep End-to-end Causal Inference
Tomas Geffner, Javier Antoran, Adam Foster +9
Causal inference is essential for data-driven decision making across domains such as business engagement, medical treatment and policy making. However, research on causal discovery…
cs.LG2020
Bayesian Deep Learning via Subnetwork Inference
Erik Daxberger, Eric Nalisnick, James Urquhart Allingham +2
The Bayesian paradigm has the potential to solve core issues of deep neural networks such as poor calibration and data inefficiency. Alas, scaling Bayesian inference to large weigh…